Scaling Laws for Predicting Downstream Performance in LLMs (2410.08527v1)

Abstract: Precise estimation of downstream performance in LLMs prior to training is essential for guiding their development process. Scaling laws analysis utilizes the statistics of a series of significantly smaller sampling LLMs (LMs) to predict the performance of the target LLM. For downstream performance prediction, the critical challenge lies in the emergent abilities in LLMs that occur beyond task-specific computational thresholds. In this work, we focus on the pre-training loss as a more computation-efficient metric for performance estimation. Our two-stage approach consists of first estimating a function that maps computational resources (e.g., FLOPs) to the pre-training Loss using a series of sampling models, followed by mapping the pre-training loss to downstream task Performance after the critical "emergent phase". In preliminary experiments, this FLP solution accurately predicts the performance of LLMs with 7B and 13B parameters using a series of sampling LMs up to 3B, achieving error margins of 5% and 10%, respectively, and significantly outperforming the FLOPs-to-Performance approach. This motivates FLP-M, a fundamental approach for performance prediction that addresses the practical need to integrate datasets from multiple sources during pre-training, specifically blending general corpora with code data to accurately represent the common necessity. FLP-M extends the power law analytical function to predict domain-specific pre-training loss based on FLOPs across data sources, and employs a two-layer neural network to model the non-linear relationship between multiple domain-specific loss and downstream performance. By utilizing a 3B LLM trained on a specific ratio and a series of smaller sampling LMs, FLP-M can effectively forecast the performance of 3B and 7B LLMs across various data mixtures for most benchmarks within 10% error margins.

Scaling Laws for Predicting Downstream Performance in LLMs

The paper focuses on the challenge of predicting downstream performance in LLMs using scaling laws. This approach integrates statistical data from smaller sampling models to anticipate the performance of larger models. Precise prediction is crucial due to the computational resource management in developing such models, where emergent abilities appear beyond specific task-dependent thresholds.

Methodology

The research introduces a two-stage process for performance estimation:

1. FLOPs to Loss Prediction: This stage involves estimating the pre-training loss based on computational resources measured in floating-point operations per second (FLOPs). Models are sampled from the same family, and a power-law relation is established, utilizing data points to fit the predictive curve effectively.
2. Loss to Performance Prediction: Leveraging the relationship between pre-training loss and downstream performance, this stage deploys data from intermediate checkpoints of pre-trained models. By collecting accurate loss-performance indicators beyond the emergent phase, the model predicts larger LLMs' performances with sound precision.

Empirical Findings

Experiments demonstrate that the proposed framework, termed FLP, accurately predicts the performance of up to 13B parameter models using smaller 3B models. Specifically, error margins are maintained within 5% and 10% for 7B and 13B models, significantly surpassing traditional FLOPs-to-Performance approaches.

Further, a variant, FLP-M, accommodates data mixtures by employing domain-specific loss analytics. This advancement allows for enhanced prediction across datasets blending general corpora and code data, addressing the integration challenges of multiple data sources during LLM training. FLP-M uses a two-layer neural network to model nonlinear relationships between domain-specific losses and task performance, yielding effective predictions with a 10% error margin across varied benchmarks.

Implications

The implications of this paper are multifaceted:

• Theoretical: It enhances the understanding of LLM scaling laws, focusing not only on computational efficiency but also on mapping emergent behaviors and performance.
• Practical: This approach provides a viable pathway to allocate resources effectively, focusing on pre-training checkpoints to gather meaningful performance data. This efficiency could translate into optimizing training budgets and workflows in large-scale LLM development.
• Future Directions: The paper suggests potential advancements in exploring diverse data mixing using FLP-M. The applicability to other data sources and combinations, while dealing with the growing complexity and diversity of pre-training datasets, can further refine the predictive accuracy of LLM capacities.

Conclusion

The paper offers an insightful forecast model for downstream performance in LLMs, utilizing scaling laws and emergent behavior insights. FLP and its variant FLP-M demonstrate a capacity for reducing computational waste while ensuring effective model performance prediction. This contribution is a significant step forward in aligning theoretical scaling laws with practical LLM development constraints, offering a robust framework to guide future LLM training strategies.